class_docstr: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
  Args:
      is_dlib(bool, optional): If true, dlib is used for detecting faces.
      prefix(str, optional): If run at another folder, the absolute path is needed to load the data.
class_docstr_tok: ["Joint","3D","Face","Reconstruction","and","Dense","Alignment","with","Position","Map","Regression","Network","Args",":","is_dlib","(","bool",",","optional",")",":","If","true",",","dlib","is","used","for","detecting","faces",".","prefix","(","str",",","optional",")",":","If","run","at","another","folder",",","the","absolute","path","is","needed","to","load","the","data","."]
docstr: Args:
      texture: the texture map. shape = (256, 256, 3).
  Returns:
      colors: the corresponding colors of vertices. shape = (num of points, 3). n is 45128 here.
docstr_tok: ["Args",":","texture",":","the","texture","map",".","shape","=","(","256",",","256",",","3",")",".","Returns",":","colors",":","the","corresponding","colors","of","vertices",".","shape","=","(","num","of","points",",","3",")",".","n","is","45128","here","."]
code: def get_colors_from_texture(self, texture):
      """
      Args:
          texture: the texture map. shape = (256, 256, 3).
      Returns:
          colors: the corresponding colors of vertices. shape = (num of points, 3). n is 45128 here.
      """
      all_colors = np.reshape(texture, [self.resolution_op**2, -1])
      colors = all_colors[self.face_ind, :]
  
      return colors
  
file_path: PaddleHub/modules/image/image_processing/prnet/api.py
imports: import os
  from time import time
  import numpy
  import paddle
  from skimage.io import imread
  from skimage.io import imsave
  from skimage.transform import estimate_transform
  from skimage.transform import warp
  from .predictor import PosPrediction
label_desc: Use image node_id 8 for calling the PRN obj's underlying member method code with example usage: obj.get_colors_from_texture(texture) and returns: colors